arXiv research

New metrics differentiate effective OOD sets for training calibrated CNNs.

Ensemble of diverse CNNs detects and mitigates adversarial attacks.

In recent years, state-of-the-art methods in computer vision have utilized increasingly deep convolutional neural network architectures (CNNs), with some of the most successful models employing hundreds or even thousands of layers. A variety of pathologies such as vanishing/exploding gradients make training such deep n…

ODENets are more robust to perturbations and adversarial attacks compared to CNNs.

Optimal hedging strategies for exotic options using vanilla options.

Vanilla GANs are connected to Wasserstein distance for better understanding.

This paper uses basket option formulas to price vanilla options with discrete dividends.

DDSME outperforms SME in estimating multimodal distributions.

ConViT combines CNN and ViT strengths, improving image classification.

New algorithm improves CRF inference and learning.

Deep vanilla transformers trained without shortcuts achieve similar performance to standard models.

Vanilla Bayesian optimization performs well in high dimensions.

New method uses neural networks for better financial hedging.

Vanilla SGD learns SIM from anisotropic data without explicit covariance estimation.

Algorithm improves vanilla option pricing accuracy during and before COVID-19.

The Bass model is calibrated to vanilla options using a fixed-point equation.

New FX option interpolations impact implied volatilities.

New approximative kernels improve PDE-G-CNNs for geometric deep learning.

In recent years, deep learning poses a deep technical revolution in almost every field and attracts great attentions from industry and academia. Especially, the convolutional neural network (CNN), one representative model of deep learning, achieves great successes in computer vision and natural language processing. How…

We attempt to interpret how adversarially trained convolutional neural networks (AT-CNNs) recognize objects. We design systematic approaches to interpret AT-CNNs in both qualitative and quantitative ways and compare them with normally trained models. Surprisingly, we find that adversarial training alleviates the textur…

Convolutional Neural Networks (CNNs) have revolutionized performances in several machine learning tasks such as image classification, object tracking, and keyword spotting. However, given that they contain a large number of parameters, their direct applicability into low resource tasks is not straightforward. In this w…

Simplified Butterfly-Net2 improves CNN efficiency in solving PDEs and signal processing tasks.

2D CNNs approximate Korobov functions with near-optimal rates.

In this paper, we argue that, once the costs of maintaining the hedging portfolio are properly taken into account, semi-static portfolios should more properly be thought of as separate classes of derivatives, with non-trivial, model-dependent payoff structures. We derive new integral representations for payoffs of exot…

This paper examines Bachelier implied volatility at extreme strikes.

In image classification, visual separability between different object categories is highly uneven, and some categories are more difficult to distinguish than others. Such difficult categories demand more dedicated classifiers. However, existing deep convolutional neural networks (CNN) are trained as flat N-way classifi…

Proposes a fixed smooth convolutional layer to reduce checkerboard artifacts in CNNs.

In this paper, we address the issue of how to enhance the generalization performance of convolutional neural networks (CNN) in the early learning stage for image classification. This is motivated by real-time applications that require the generalization performance of CNN to be satisfactory within limited training time…

TinyCNN accelerates CNN models on embedded FPGA with 15x speedup.

Simple 1D-CNN network predicts electricity loads 36 hours ahead.

This paper compares two GAN architectures to reduce mode collapse.

This paper proposes a framework based on deep convolutional neural networks (CNNs) for automatic heart sound classification using short-segments of individual heart beats. We design a 1D-CNN that directly learns features from raw heart-sound signals, and a 2D-CNN that takes inputs of two- dimensional time-frequency fea…

This paper presents an unsupervised method to learn a neural network, namely an explainer, to interpret a pre-trained convolutional neural network (CNN), i.e., the explainer uses interpretable visual concepts to explain features in middle conv-layers of a CNN. Given feature maps of a conv-layer of the CNN, the explaine…

State-of-the-art image recognition systems use sophisticated Convolutional Neural Networks (CNNs) that are designed and trained to identify numerous object classes. Such networks are fairly resource intensive to compute, prohibiting their deployment on resource-constrained embedded platforms. On one hand, the ability t…

Convolutional neural networks (CNN) have achieved state of the art performance on both classification and segmentation tasks. Applying CNNs to microscopy images is challenging due to the lack of datasets labeled at the single cell level. We extend the application of CNNs to microscopy image classification and segmentat…

New method trains deep vanilla networks as fast as ResNets without shortcut connections.

Quantization algorithms have been successfully adopted to option pricing in finance thanks to the high convergence rate of the numerical approximation. In particular, very recently, recursive marginal quantization has been proven to be a flexible and versatile tool when applied to stochastic volatility processes. In th…

Improved robustness of 1D CNNs for heart arrhythmia classification.

Efficient CNN for VQA achieves similar performance to standard models.

The paper finds optimal strategies for hedging in incomplete markets using derivatives.

Paper examines fairness of data augmentation methods, finding vanilla Mixup outperforms Fair Mixup.

Computer vision performances have been significantly improved in recent years by Convolutional Neural Networks(CNN). Currently, applications using CNN algorithms are deployed mainly on general purpose hardwares, such as CPUs, GPUs or FPGAs. However, power consumption, speed, accuracy, memory footprint, and die size sho…

This paper proposes a generic method to learn interpretable convolutional filters in a deep convolutional neural network (CNN) for object classification, where each interpretable filter encodes features of a specific object part. Our method does not require additional annotations of object parts or textures for supervi…

This paper reports the performances of shallow word-level convolutional neural networks (CNN), our earlier work (2015), on the eight datasets with relatively large training data that were used for testing the very deep character-level CNN in Conneau et al. (2016). Our findings are as follows. The shallow word-level CNN…

We study option pricing and hedging with uncertainty about a Black-Scholes reference model which is dynamically recalibrated to the market price of a liquidly traded vanilla option. For dynamic trading in the underlying asset and this vanilla option, delta-vega hedging is asymptotically optimal in the limit for small u…

Recently, Convolutional Neural Networks (CNNs) demonstrate a considerable vulnerability to adversarial attacks, which can be easily misled by adversarial perturbations. With more aggressive methods proposed, adversarial attacks can be also applied to the physical world, causing practical issues to various CNN powered a…

Counterfactual regret minimization (CFR) is the most popular algorithm on solving two-player zero-sum extensive games with imperfect information and achieves state-of-the-art performance in practice. However, the performance of CFR is not fully understood, since empirical results on the regret are much better than the …

Study on CNNs' learning rates and approximation capacities.